Skip to main content

Advertisement

SpringerLink
Log in

Uplink scheduling of MU-MIMO gateway for massive data acquisition in Internet of things

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Due to the uplink-centric characteristic of diverse Internet of things (IoT) applications, the wireless connectivity for the IoT should provide a sufficient data rate for the delivery of a huge amount of data traffic toward the uplink direction; furthermore, it needs to satisfy the low complexity and high energy efficiency for the deployment of resource-constrained IoT devices. To this end, we propose an IoT uplink multiple-input and multiple-output (MIMO) scheduling scheme (IoT-UL-MIMO) for which a multi-user (MU)-MIMO capability is implemented in the IoT-gateway device to greatly extend the uplink bandwidth and support the massive data delivery from a plurality of devices toward the gateway. In the IoT-UL-MIMO, a simple user-selection process that selects a proper set of transmitters by just counting the number of successful control-frame transmissions from the devices is used without a channel-sensing mechanism. The aim of this design principle is a mitigation of the system complexity that reduces the energy consumption, which is a key consideration for resource-constrained IoT devices. The simulation results show significant improvements for the throughput and energy efficiency of IoT-UL-MIMO. A duration-length optimization regarding the uplink scheduling phase is also discussed in this paper.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Abid B, Nguyen TT, Seba H (2015) New data aggregation approach for time-constrained wireless sensor networks. J Supercomput 71(5):1678–1693

    Article  Google Scholar 

  2. Chen M (2013) Towards smart city: M2M communications with software agent intelligence. Multimed Tools Appl 67(1):167–178

    Article  Google Scholar 

  3. Kim T-Y, Kim E-J (2015) Multi-hop WBAN configuration approach for wearable machine-to-machine systems. Multimed Tools Appl. doi:10.1007/s11042-015-2832-x

  4. Kwon J-H, Chang HS, Shon T, Jung J-J, Kim E-J (2016) Neighbor stability-based VANET clustering for urban vehicular environments. J Supercomput 72(1):161–176

    Article  Google Scholar 

  5. Park H, Kim E-J (2015) Location-oriented multiplexing transmission for capillary machine-to-machine systems. Multimed Tools Appl. doi:10.1007/s11042-015-2833-9

  6. Chen Y, Lee GM, Shu L, Crespi N (2016) Industrial Internet of things-based collaborative sensing intelligence: framework and research challenges. Sensors 16(2):1–19

    Article  Google Scholar 

  7. Suryadevara NK, Mukhopadhyay SC, Kelly SDT, Gill SPS (2015) WSN-based smart sensors and actuator for power management in intelligent buildings. IEEE/ASME Trans Mechatron 20(2):564–571

    Article  Google Scholar 

  8. Jung M, Han K, Cho J (2015) Advanced verification on WBAN and cloud computing for u-health environment. Multimed Tools Appl 74(16):6151–6168

    Article  Google Scholar 

  9. Nevat I, Peters GW, Septier F, Matsui T (2015) Estimation of spatially correlated random fields in heterogeneous wireless sensor networks. IEEE Trans Signal Process 63(10):2597–2609

    Article  MathSciNet  Google Scholar 

  10. Rani S, Talwar R, Malhotra J, Ahmed SH, Sarkar M, Song H (2015) A novel scheme for an energy efficient Internet of things based on wireless sensor networks. Sensors 15(11):28603–28626

    Article  Google Scholar 

  11. Neto JBB, Silva TH, Assuncao RM, Mini RAF, Loureiro AAF (2015) Sensing in the collaborative Internet of things. Sensors 15(3):6607–6632

    Article  Google Scholar 

  12. Smith DB, Lamahewa T, Hanlen LW, Miniutti D (2011) Simple prediction-based power control for the on-body area communications channel. In: Proc. ICC’11, pp 1–5

  13. Rashwand S, Misic J (2011) Performance evaluation of IEEE 802.15.6 under non-saturation condition. In: Proc. GLOBECOM’11, pp 1–6

  14. Kumar A, Srivastava V, Singh MK, Hancke GP (2015) Current status of the IEEE 1451 standard-based sensor applications. IEEE Sens J 15(5):2505–2513

    Article  Google Scholar 

  15. Oh J-M, Moon N, Hong S (2015) Trajectory based database management for intelligent surveillance system with heterogeneous sensors. Multimed Tools Appl. doi:10.1007/s11042-015-2725-z

  16. Depuy C (2015) Internet of things (IoT) and wireless LAN (WLAN) go hand-in-hand. http://www.delloro.com/chris-depuy/internet-of-things-iot-and-wireless-lan-wlan-go-hand-in-hand. Accessed 1 Dec 2015

  17. 802.11ac (2013) IEEE standard for information technology—telecommunications and information exchange between systems—local and metropolitan area networks—specific requirements—part 11: wireless LAN medium access control (MAC) and physical layer (PHY) specifications—amendment 4: enhancements for very high throughput for operation in bands below 6 GHz

  18. Zhou S, Niu Z (2010) Distributed medium access control with SDMA support for WLANs. IEICE Trans Commun E93–B(4):961–970

    Article  Google Scholar 

  19. Liao R, Bellalta B, Oliver M (2012) DCF/USDMA: enhanced DCF for uplink SDMA transmissions in WLANs. In: Proc. IWCMC’12, pp 263–268

  20. Xie X, Zhang X (2014) Scalable user selection for MU-MIMO networks. In: Proc. INFOCOM’14, pp 808–816

  21. Huang S, Yin H, Wu J, Leung VCM (2013) User selection for multiuser MIMO downlink with zero-forcing beamforming. IEEE Trans Veh Technol 62(7):3084–3097

    Article  Google Scholar 

  22. Bayesteh A, Member S, Khandani AK (2008) On the user selection for MIMO broadcast channels. IEEE Trans Inf Theory 54(3):1086–1107

    Article  MathSciNet  MATH  Google Scholar 

  23. Love DJ, Heath RW, Lau VKN, Gesbert D, Rao BD, Andrews M (2008) An overview of limited feedback in wireless communication systems. IEEE J Sel Area Commun 26(8):1341–1365

    Article  Google Scholar 

  24. Gong MX, Perahia E, Want R, Mao S (2010) Training protocols for multi-user MIMO wireless LANs. In: Proc. PIMRC’10, pp 1218–1223

  25. Lou H, Ghosh M, Xia P, Olesen R (2013) A comparison of implicit and explicit channel feedback methods for MU-MIMO WLAN systems. In: Proc. PIMRC’13, pp 419–424

  26. Halperin D, Greenstein B, Sheth A, Wetherall D (2010) Demystifying 802.11n power consumption. In: Proc. HotPower’10, pp 1–5

Download references

Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF- 2014R1A1A2057641). This research was also supported by Hallym University Research Fund, 2015 (H20150666).

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, South Korea

    Tae-Yoon Kim

  2. Department of Convergence Software, Hallym University, 1 Hallymdaehak-gil, Chuncheon-si, Gangwon-do, 24252, South Korea

    Eui-Jik Kim

Authors
  1. Tae-Yoon Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eui-Jik Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eui-Jik Kim.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, TY., Kim, EJ. Uplink scheduling of MU-MIMO gateway for massive data acquisition in Internet of things. J Supercomput 74, 3549–3563 (2018). https://doi.org/10.1007/s11227-016-1716-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-016-1716-9

Keywords

Search

Navigation